{"created":"2025-01-18T23:24:38.792013+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00062749","sets":["1164:4619:5663:5733"]},"path":["5733"],"owner":"10","recid":"62749","title":["カーネル回帰に基づくカラー画像補間"],"pubdate":{"attribute_name":"公開日","attribute_value":"2009-08-24"},"_buckets":{"deposit":"da2998cf-50d5-4cce-99d7-d604f9942af5"},"_deposit":{"id":"62749","pid":{"type":"depid","value":"62749","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"カーネル回帰に基づくカラー画像補間","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"カーネル回帰に基づくカラー画像補間"},{"subitem_title":"Color Image Interpolation using Kernel Regression","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2009-08-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学大学院理工学研究科機械制御システム専攻"},{"subitem_text_value":"東京工業大学大学院理工学研究科機械制御システム専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, Tokyo Institute of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/62749/files/IPSJ-CVIM09168020.pdf"},"date":[{"dateType":"Available","dateValue":"2011-08-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM09168020.pdf","filesize":[{"value":"549.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"912f4b49-4e3d-4ed6-9d1f-58b7db99ae5f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2009 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田中, 正行"},{"creatorName":"奥富, 正敏"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masayuki, Tanaka","creatorNameLang":"en"},{"creatorName":"Masatoshi, Okutomi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，適応的カーネルを利用したカーネル回帰法が，デノイズおよび画像補間の分野で注目されている．しかしながら，従来のカーネル回帰法は，主にグレイ画像に対する手法であり，カラー画像に対しては，チャネル毎に独立にカーネル回帰法が適用されている．そこで，本論文では，チャネル間の相関を利用した回帰関数モデルを提案する．また，従来手法では，カーネル関数は画像のテクスチャに適応的に設計されていた．しかしながら，特に不規則サンプリングされたデータを補間する場合，データ密度が画素位置により異なるため，カーネル関数の大きさを適応的に設計する必要がある．そのため，本論文では，カーネル関数の大きさを関数回帰の安定性に基づき適応的に設計する方法を提案する．さらに，入力データと補間画像データを融合し，繰り返し処理を行う方法も提案する．提案手法と従来の適応的カーネルを利用したカーネル回帰法と比較し，提案手法の有効性を確認した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A kernel regression with adaptive kernel is known as a powerful tool in a low-level vision which includes denoising and image interpolation. For color images, the kernel regression for a gray image is independently applied to each color channel, because the kernel regression has been developed for the gray image. In this paper, we propose a color kernel regression whose regression function is modeled using color correlations. For the interpolation, the size of kernel function should be large for sparse data region and small for dense data region. Therefore, we also propose a kernel design algorithm based on the stability of the regression. We experimentally show that the proposed color kernel regression outperforms existing kernel regressions.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2009-08-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"20","bibliographicVolumeNumber":"2009-CVIM-168"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"id":62749,"updated":"2025-01-22T02:28:36.529637+00:00","links":{}}